Surrogate-Aided Learning of Active Tether-Net Maneuver to Capture Rotating Space Debris

Active tether-net systems offer a promising solution to the capture and disposal of large space debris that pose significant safety risks to the operation of satellites in Earth’s orbit. These systems comprise a flexible net with maneuverable units (MUs) launched from a small chaser spacecraft, with the MUs assumed to have independent thrust capabilities, such as based on cold gas propulsion. The requirement of autonomous operation of this system that must also work across varying relative location and rotation of the target debris makes the capture process challenging. Given these requirements and the complexity of modeling the deployment dynamics of the net and net/debris interactions, this article presents neural-network-based policies to guide the maneuvering (thrust) actions of the MUs, which are trained using reinforcement learning (RL). To this end, the tether-net maneuver operation is modeled as a Markov decision process with the state encompassing the target’s location, orientation, and rotation rates relative to the chaser, and solved using a policy gradient approach. A special reward function is formulated to account for both the primary objectives of capture success metrics and fuel consumptions incurred by the MUs, and additional objectives related to hypothesized favorable intermediate states of the net that facilitate capture performance and alleviate reward sparsity during RL training. To avoid the otherwise prohibitive computing cost of training the maneuver policy via RL, surrogate models based on long short-term memory and convolutional neural networks are trained for predicting respectively the deployment trajectory and capture outcome; these are used in various combinations with a higher fidelity simulation as the RL environment. Significant improvement in capture performance, from around 5.7% (with random actions) to 85%, is achieved via training. Tested over unseen scenarios, the best RL-derived policy readily outperforms an aiming-point-based baseline, with the action profiles providing important insights into net behavior — e.g., active net rotation and maximal mouth opening — favorable to capture success and low fuel consumption by MUs.